Systems and methods for facial property identification

ABSTRACT

Systems and methods are provided for facial property identification. For example, an image sample is acquired; a first effective area image of a face is acquired in the image sample; first textural features of the first effective area image are extracted; and the first textural features of the first effective area image are classified by race, gender and age using a race classifier, a gender classifier and an age classifier successively to obtain a race property, a gender property and an age property of the face.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority to Chinese Patent Application No.201310172492.8, filed May 10, 2013, incorporated by reference herein forall purposes.

FIELD OF THE INVENTION

Certain embodiments of the present invention are directed to computertechnology. More particularly, some embodiments of the invention providesystems and methods for facial property identification. But it would berecognized that the invention has a much broader range of applicability.

BACKGROUND OF THE INVENTION

With the development of information technology and the popularity ofnetwork technology, people increasingly use various types of imagecollection equipments in daily lives, such as a surveillance camera, adigital camcorder, a network camera, a digital camera, a cell phonecamera and a video sensor of Internet-of-Things, to acquire a largeamount of images and video data. Rapid and intelligent analysis of sucha large amount of images and video data is urgently needed.

Facial identification technology can analyze images and video dataintelligently. Race, gender and age are three major properties forfacial identification because these properties describe a socialbackground, behavioral principles and a living status of a person. Thesemajor properties can be reflected on a person's face. A solution isneeded regarding how to obtain data related to these three propertiesaccording to certain input images.

Hence it is highly desirable to improve the techniques for facialproperty identification.

BRIEF SUMMARY OF THE INVENTION

According to one embodiment, a method is provided for facial propertyidentification. For example, an image sample is acquired; a firsteffective area image of a face is acquired in the image sample; firsttextural features of the first effective area image are extracted; andthe first textural features of the first effective area image areclassified by race, gender and age using a race classifier, a genderclassifier and an age classifier successively to obtain a race property,a gender property and an age property of the face.

According to another embodiment, a system for facial propertyidentification includes: a test-sample-acquisition module, aneffective-area-image-acquisition module, a textural-feature-extractionmodule, and a facial-property-identification module. Thetest-sample-acquisition module is configured to acquiring an imagesample. The effective-area-image-acquisition module is configured toacquire a first effective area image of a face in the image sample. Thetextural-feature-extraction module is configured to extract firsttextural features of the first effective area image. Thefacial-property-identification module is configured to classify thefirst textural features of the first effective area image by race,gender and age using a race classifier, a gender classifier and an ageclassifier successively to obtain a race property, a gender property andan age property of the face.

According to yet another embodiment, a non-transitory computer readablestorage medium includes programming instructions for facial propertyidentification. The programming instructions are configured to cause oneor more data processors to execute operations. For example, an imagesample is acquired; a first effective area image of a face is acquiredin the image sample; first textural features of the first effective areaimage are extracted; and the first textural features of the firsteffective area image are classified by race, gender and age using a raceclassifier, a gender classifier and an age classifier successively toobtain a race property, a gender property and an age property of theface.

For example, the systems and methods provided herein are configured toperform facial property identification using race classifiers, genderclassifiers and age classifiers to replace a traditional manual methodor a traditional equipment inspection method used in security monitoringso as to improve efficiency and accuracy and reduce cost.

Depending upon embodiment, one or more benefits are achieved. Thesebenefits and various additional objects, features and advantages of thepresent invention are fully appreciated with reference to the detaileddescription and accompanying drawings that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified diagram showing a method for facial propertyidentification according to one embodiment of the present invention;

FIG. 2 is a simplified diagram showing a method for facial propertyidentification according to another embodiment of the present invention;

FIG. 3 is a simplified diagram showing certain gabor images according toone embodiment of the present invention;

FIG. 4 is a simplified diagram showing a process for extracting BIMfeatures from a gabor image according to one embodiment of the presentinvention:

FIG. 5 is a simplified diagram showing a system for facial propertyidentification according to one embodiment of the present invention;

FIG. 6 is a simplified diagram showing a system for facial propertyidentification according to another embodiment of the present invention;and

FIG. 7 is a simplified diagram showing a property-classifier-trainingmodule as part of a system for facial property identification accordingto one embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a simplified diagram showing a method for facial propertyidentification according to one embodiment of the present invention.This diagram is merely an example, which should not unduly limit thescope of the claims. One of ordinary skill in the art would recognizemany variations, alternatives, and modifications. The method 100includes at least the process 101 for acquiring an image sample, theprocess 102 for acquiring a first effective area image of a face in theimage sample, the process 103 for extracting first textural features ofthe first effective area image, and the process 104 for classifying thefirst textural features of the first effective area image by race,gender and age using a race classifier, a gender classifier and an ageclassifier successively to obtain a race property, a gender property andan age property of the face.

According to one embodiment, before the process 102, the method 100further includes a process for establishing a race classifier, a genderclassifiers and an age classifier successively. For example, the processfor establishing a race classifier, a gender classifiers and an ageclassifier successively includes: acquiring a training image samplecollection; acquiring a second effective area image in an image samplein the training image sample collection; extracting second texturalfeatures of the second effective area image; and establishing the raceclassifier, the gender classifier and the age classifier successivelybased on at least information associated with the second texturalfeatures of the second effective area images in the training imagesample collection.

According to another embodiment, the process for establishing the raceclassifier based on at least information associated with the secondtextual features in the training image sample collection includes:dividing the training sample collection into white training samples,black training samples and yellow training samples corresponding to awhite race, a black race and a yellow race respectively; and trainingthird textural features of the white training samples, the blacktraining samples and the yellow training samples separately to obtain aternary classifier associated with the white race, the black race andthe yellow race. For example, the process for establishing the genderclassifier based on at least information associated with the secondtextural features in the training image sample collection includes:dividing the white training samples, the black training samples and theyellow training samples by gender to obtain gender samples of each race;and training fourth textural features of the gender samples of each raceseparately to obtain a gender binary classifier associated with eachrace, wherein the binary classifier corresponds to male and female. Inanother example, the process for establishing the age classifier basedon at least information associated with the second textural features inthe training image sample collection includes: dividing the gendersamples of each race by age groups to obtain infant training samples,child training samples, youth training samples and senior trainingsamples; training the infant training samples, the child trainingsamples, the youth training samples and the senior training samples toestablish a first-level age classifier; training fifth textural featuresof second-level training samples associated with an age interval of fiveyears related to the infant training samples, the child trainingsamples, the youth training samples and the senior training samples toestablish a second-level age classifier; and training a linearly-fitthird-level age classifier for the infant training samples, the childtraining samples, the youth training samples and the senior trainingsamples based on at least information associated with an age interval offive years.

According to yet another embodiment, the process 102 includes: detectingthe face in the image sample; determining eye positions on the face;calibrating an original image of the face based on the eye positions onthe face; and obtaining the first effective area image of the facewithin a predetermined area centering on the eyes of the face. Forexample, the process for extracting first textural features of the firsteffective area image includes: extracting biologically inspired model(BIM) features within the first effective area image of the face. Inanother example, the process for extracting BIM features within thefirst effective area image of the face includes:

a) establishing 64 sets of gabor filters and filtering the firsteffective area image of the face in 16 scales and 4 orientations usingthe 64 sets of gabor filters to obtain a gabor image;b) dividing the gabor image into 8 parts, wherein a part includes twoscales and four orientations;c) in an orientation of a part, selecting one set of m×n masks anddividing the gabor image to obtain two sets of serial gabor features;andd) comparing gabor features corresponding to the two scales of the partand taking a higher value of corresponding feature dimensions as a finalfeature output.

In addition, the process for extracting BIM features within the firsteffective area image of the face includes: adjusting sizes of the masks;and repeating the operations c)-d) for k times to obtain k×4×8 sets ofBIM features of the first effective area image of the face. For example,m, n and k are positive integers. In some embodiments, the method 100further includes a process for performing principal component analysis(PCA) dimensionality reduction on the k×4×8 sets of BIM features toobtain the first textual features.

In one embodiment, the process 104 includes: classifying the firsttextural features of the first effective area image by race with a raceclassifier to obtain a race property of the face; selecting a genderclassifier corresponding to the race property of the face; classifyingthe first textual features of the first effective area image by genderwith the gender classifier to obtain a gender property of the face;selecting an age classifier corresponding to the race property and thegender property of the face; and classifying the first textural featuresof the first effective area image by age with the age classifier toobtain an age property of the face. For example, the process forselecting a gender classifier corresponding to the race property of theface and the classifying the first textual features of the firsteffective area image by gender with the gender classifier to obtain agender property of the face includes: selecting a first-level ageclassifier corresponding to the race property and the gender property ofthe face; inputting the first textural features of the first effectivearea image into the first-level age classifier to obtain first-level agegroups corresponding to the first effective area image and first weightsof the first-level age groups; selecting a second-level age classifieraccording to a particular first-level age group with a highest firstweight among the first-level age groups; inputting the first texturalfeatures of the first effective area image into the second-level ageclassifier to obtain second-level age groups corresponding to the firsteffective area image and second weights of the second-level age groups;selecting a third-level age classifier according to a particularsecond-level age group with a highest second weight among thesecond-level age groups; inputting the first textural features of thefirst effective area image into the third-level age classifier to obtaina third-level age corresponding to the first effective area image; andobtaining an age property of the face according to the third-level age,the first weights of the first-level age groups and the second weightsof the second-level age groups.

FIG. 2 is a simplified diagram showing a method for facial propertyidentification according to another embodiment of the present invention.This diagram is merely an example, which should not unduly limit thescope of the claims. One of ordinary skill in the art would recognizemany variations, alternatives, and modifications. The method 200includes at least the processes 201-205.

According to one embodiment, during the process 201, a training imagesample collection is acquired, and second textual features of thetraining image sample collection are extracted. For example, thetraining image sample collection includes a plurality of facial imagesof different races, genders and ages. As an example, the facial imagesinclude photos and frames in a video stream. In some embodiments, theprocess for extracting second textual features of the training imagesample collection includes: acquiring a second effective area image inan image sample in the training image sample collection; and extractingsecond textural features of the second effective area image. Forexample, the process for acquiring a second effective area image in animage sample in the training image sample collection includes: detectinga face in the image sample; determining eye positions on the face;calibrating an original image of the face based on the eye positions onthe face; and obtaining the second effective area image of the facewithin a predetermined area centering on the eyes of the face. As anexample, the predetermined are covers an effective area image in a sizeof 64×64 centering the eyes. In certain embodiments, an adaboostalgorithm and a haar characteristic algorithm are used to acquire theposition of a facial frame in the original image and then the positionof eyes in the facial frame. In another example, a 64×64 effective areacentered on the eyes is extracted as the facial image. As an example,the facial image is subject to a Point Divid Arithmetic Mean (PDAM)illumination treatment in order to preclude environmental interferences.

FIG. 3 is a simplified diagram showing certain gabor images according toone embodiment of the present invention. This diagram is merely anexample, which should not unduly limit the scope of the claims. One ofordinary skill in the art would recognize many variations, alternatives,and modifications.

FIG. 4 is a simplified diagram showing a process for extracting BIMfeatures from a gabor image according to one embodiment of the presentinvention. This diagram is merely an example, which should not undulylimit the scope of the claims. One of ordinary skill in the art wouldrecognize many variations, alternatives, and modifications.

Referring back to FIG. 2, the process for extracting second texturalfeatures of the second effective area image includes: extracting BIMfeatures within the second effective area image of the face, in someembodiments. For example, the process for extracting BIM features withinthe second effective area image of the face includes:

a) establishing 64 sets of gabor filters and filtering the secondeffective area image of the face in 16 scales and 4 orientations usingthe 64 sets of gabor filters to obtain a gabor image (e.g., as shown inFIG. 3);b) dividing the gabor image into 8 parts, wherein a part includes twoscales and four orientations;c) in an orientation of a part, selecting one set of m×n masks anddividing the gabor image to obtain two sets of serial gabor features;andd) comparing gabor features corresponding to the two scales of the partand taking a higher value of corresponding feature dimensions as a finalfeature output.

In addition, the process for extracting BIM features within the secondeffective area image of the face includes: adjusting sizes of the masks;and repeating the operations c)-d) for k times to obtain k sets of BIMfeatures, in some embodiments. For example, k×4 sets of BIM features areobtained for each part. As shown in FIG. 4, eventually, k×4×8 sets ofBIM features of the second effective area image of the face areobtained. For example, m, n and k are positive integers. As an example,in the calculation of BIM features, filter parameters and mask sizes canbe adjusted.

In one embodiment, if feature dimensions are too high, eight parts areintegrated as one part so that the feature in each orientationcorresponds to a highest value of the features of different masks toeventually obtain k×4 sets of features. For example, after obtainingk×4×8 sets of BIM features of the second effective area image of theface, principal component analysis (PCA) dimensionality reduction isperformed on the k×4×8 sets of BIM features to obtain the second textualfeatures.

Referring back to FIG. 2, during the process 202, the race classifier,the gender classifier and the age classifier are establishedsuccessively based on at least information associated with the secondtextural features of the second effective area images in the trainingimage sample collection. For example, the race classifier, the genderclassifier and the age classifier are established successively based onall training samples, and based on various methods, such as adecision-making tree and a gentleboost. As shown in FIG. 3, a SupportVector Machine (SVM) classifier is used, in some embodiments.

In one embodiment, the process for establishing the race classifierbased on at least information associated with the second textualfeatures in the training image sample collection includes: dividing thetraining sample collection into white training samples, black trainingsamples and yellow training samples corresponding to a white race, ablack race and a yellow race respectively; and training third texturalfeatures of the white training samples, the black training samples andthe yellow training samples separately to obtain a ternary classifierassociated with the white race, the black race and the yellow race. Forexample, positive samples of each classifier correspond to all samplesof a particular race and negative samples correspond to all samples forother races. As an example, training is performed on the positivesamples and the negative samples to obtain a ternary classifier asfollows:

$\begin{matrix}{{f_{t}(x)} = \left\{ {{\begin{matrix}0 & {x \notin {Sam}_{t}} \\1 & {x \in {Sam}_{t}}\end{matrix}\mspace{14mu} t} = \left\{ {w,b,y} \right\}} \right.} & (1)\end{matrix}$

where f_(w), f_(b), f_(y) correspond to a classifier for each racerespectively, and Sam_(w) represents sample features of a correspondingrace. For example, the race classification result corresponds to a sumof outputs of all race classifiers as follows:

f _(race)(x)=Σ(f _(w)(x)+f _(b)(x)+f _(y)(x))  (2)

In another embodiment, the process for establishing the genderclassifier based on at least information associated with the secondtextural features in the training image sample collection includes:dividing the white training samples, the black training samples and theyellow training samples by gender to obtain gender samples of each race;and training fourth textural features of the gender samples of each raceseparately to obtain a gender binary classifier associated with eachrace, wherein the binary classifier corresponds to male and female. Forexample, a gender classifier is trained for each race, which means thereare three binary classifiers f_(gender) ^(w), f_(gender) ^(b),f_(gender) ^(y). As an example, for all white samples, white males aretaken as the positive samples and white females as the negative samples.The positive samples and the negative samples are trained to obtain theclassification function for the white race f_(gender) ^(w) as follows:

$\begin{matrix}{{f_{gender}^{w}(x)} = \left\{ \begin{matrix}0 & {x \in {Sam}_{w}^{f}} \\1 & {x \in {Sam}_{w}^{m}}\end{matrix} \right.} & (3)\end{matrix}$

Similarly, the classification functions for the black race and theyellow race are trained with the black samples and the yellow samplesrespectively, in some embodiments.

$\begin{matrix}{{f_{gender}^{b}(x)} = \left\{ \begin{matrix}0 & {x \in {Sam}_{b}^{f}} \\1 & {x \in {Sam}_{b}^{m}}\end{matrix} \right.} & (4) \\{{f_{gender}^{y}(x)} = \left\{ \begin{matrix}0 & {x \in {Sam}_{y}^{f}} \\1 & {x \in {Sam}_{y}^{m}}\end{matrix} \right.} & (5)\end{matrix}$

As an example, the training samples are divided into six classes bygender under different races.

In yet another embodiment, the process for establishing the ageclassifier based on at least information associated with the secondtextural features in the training image sample collection includes:dividing the gender samples of each race by age groups to obtain infanttraining samples, child training samples, youth training samples andsenior training samples; training the infant training samples, the childtraining samples, the youth training samples and the senior trainingsamples to establish a first-level age classifier; training fifthtextural features of second-level training samples associated with anage interval of five years related to the infant training samples, thechild training samples, the youth training samples and the seniortraining samples to establish a second-level age classifier; andtraining a linearly-fit third-level age classifier for the infanttraining samples, the child training samples, the youth training samplesand the senior training samples based on at least information associatedwith an age interval of five years. For example, the age classifiers aretrained for each gender under each race. The age classifiers includethree levels, in some embodiments. First, the training samples aredivided into infants (0-2), children (5-12), youths (20-40) and seniors(above 55) for both genders under each race. For example, the samples ofa current age group are the positive samples, f_(age) ^(0˜2)(x), f_(age)^(5˜12)(x), f_(age) ^(20˜40)(x) and f_(age) ⁵⁵(x) the samples of otherage groups are negative samples. As an example, a first-level classifieris:

f _(age) ¹(x)=Σ(f _(age) ^(0˜2)(x)+f _(age) ^(5˜12)(x)+f _(age)^(20˜45)(x)+f _(age) ^(55˜100)(x))  (6)

Then a second-level classifier f_(age) ² is established under each agegroup based on an age interval of five years, as follows:

f _(age) ²(x)=Σ(Σf _(age) ^(5*(k-1)˜5*k)(x))  (7)

where k represents a k^(th) 5-year interval under the current age group.For example, the age group of 20-40 is divided into four sub-groups andthe range of the first age sub-group is (5×(1−1)=0˜5×1=5)+20, which canbe simplified to 5×(k−1)˜5×k. In some embodiments, a third-levellinearly-fit age classifier f_(age) ³ is trained for each 5-yearinterval,

f _(age) ³ =ax ² +bx+c  (8)

As the difference is limited between different ages, direct and rigidsectioning may make the differences between the samples of two adjacentclasses very small, for example. The first-level age classifier usesdiscontinuous age sectioning, for example, infants (0-2) and children(5-12) with an interval of 3-4 years between the two age groups, so thatthe samples under each class can better describe the texturalinformation of the class to improve the performance of theclassification, in some embodiments.

In certain embodiments, the processes 201-202 correspond to the specificprocesses to establish the race classifier, the gender classifier andthe age classifier. For example, the processes 201-202 are omitted ifcertain property classifiers have been established.

According to one embodiment, during the process 203, an image sample isacquired, and a first effective area image of a face is acquired in theimage sample. For example, the facial property identification isperformed to acquire an image sample to be tested, where the imagesample includes a photo or a video stream acquired via a camcorder. Asan example, the process 203 is similar to the acquisition sub-processthe process 201.

According to another embodiment, during the process 204, first texturalfeatures of the first effective area image are extracted. The process204 is similar to the extraction sub-process of the process 201. Forexample, during the process 205, the first textural features of thefirst effective area image are classified by race, gender and age usinga race classifier, a gender classifier and an age classifiersuccessively to obtain a race property, a gender property and an ageproperty of the face. As an example, the process 205 includes:classifying the first textural features of the first effective areaimage by race with a race classifier to obtain a race property of theface; selecting a gender classifier corresponding to the race propertyof the face; classifying the first textual features of the firsteffective area image by gender with the gender classifier to obtain agender property of the face; selecting an age classifier correspondingto the race property and the gender property of the face; andclassifying the first textural features of the first effective areaimage by age with the age classifier to obtain an age property of theface.

According to yet another embodiment, the first textural features of thefirst effective area image of the face to be tested are input into theequation (3) and race=max(f_(race)(x)), so as to obtain the raceproperty of the face. For example, if the race property is yellow, agender classifier for the yellow race is selected, and the firsttextural features of the first effective area image of the face to betested are input into the equation (5) so as to obtain the genderproperty of the face. As an example, the gender property is female.

In some embodiments, the process for selecting a gender classifiercorresponding to the race property of the face and the classifying thefirst textual features of the first effective area image by gender withthe gender classifier to obtain a gender property of the face includes:selecting a first-level age classifier corresponding to the raceproperty and the gender property of the face; inputting the firsttextural features of the first effective area image into the first-levelage classifier to obtain first-level age groups corresponding to thefirst effective area image and first weights of the first-level agegroups; selecting a second-level age classifier according to aparticular first-level age group with a highest first weight among thefirst-level age groups; inputting the first textural features of thefirst effective area image into the second-level age classifier toobtain second-level age groups corresponding to the first effective areaimage and second weights of the second-level age groups; selecting athird-level age classifier according to a particular second-level agegroup with a highest second weight among the second-level age groups;inputting the first textural features of the first effective area imageinto the third-level age classifier to obtain a third-level agecorresponding to the first effective area image; and obtaining an ageproperty of the face according to the third-level age, the first weightsof the first-level age groups and the second weights of the second-levelage groups.

In certain embodiments, an age classifier f_(age) ¹ corresponding to arace property and a gender property is selected, and an age group l₁corresponding to the image sample to be tested and the weight p₁ undereach class are obtained. For example, the weight p₁ corresponds to a SVMclassification output, [l₁, p₁]=f_(age) ¹(x). As an example, allclassifier outputs are real numbers each representing a probability tobe classified into a current class and a weight corresponds to theprobability.

In some embodiments, the age groups of an image sample to be tested andthe training image samples are different in order to improve thecoverage of age groups. For the image sample to be tested, the ageranges are (0-5) for infants, (3-20) for children, (15-50) for youthsand (>45) for seniors. For the output age group l₁, a second-level ageclassifier f_(age) ² is selected under the age group to obtain agesub-groups l₂ with an interval of five years and the correspondingweight p₂, where [l₂, p₂]=f_(age) ²(x).

In certain embodiments, n age groups with highest weights are selected.For example, a corresponding age classifier f_(age) ³ for each age groupis selected to calculate the age of the current sample: age^(t)=f_(age)³ (x), t=1, 2 . . . n. As an example, the final facial age output is

age=Σage^(t) *p ₁ ^(t) *p ₂ ^(t).

In some embodiments, the method 100 and/or the method 200 can be usedfor age-based access control at Internet cafes and cinemas, or loginauthorization for limiting minors' access to some adult websites. Forexample, the method 100 and/or the method 200 can also be used toautomatically collect property data of users, e.g., in trading markets,to obtain useful social information of users which can be used forcustomer analysis. As an example, the method 100 and/or the method 200can be implemented without causing any disturbance to users. Forinstance, some malls, hotels and hospitals can use the method 100 and/orthe method 200 to analyze background information of customers andpatients to make more accurate market judgment. Furthermore, the method100 and/or the method 200 can be used for user property analysis inorder to provide more targeted advertising services and improve theaudience accuracy.

FIG. 5 is a simplified diagram showing a system for facial propertyidentification according to one embodiment of the present invention.This diagram is merely an example, which should not unduly limit thescope of the claims. One of ordinary skill in the art would recognizemany variations, alternatives, and modifications. The system 500includes: a test-sample-acquisition module 301, aneffective-area-image-acquisition module 302, atextural-feature-extraction module 303 and afacial-property-identification module 304.

According to one embodiment, the test-sample-acquisition module 301 isconfigured to acquiring an image sample. For example, theeffective-area-image-acquisition module 302 is configured to acquire afirst effective area image of a face in the image sample. In anotherexample, the textural-feature-extraction module 303 is configured toextract first textural features of the first effective area image. As anexample, the facial-property-identification module 304 is configured toclassify the first textural features of the first effective area imageby race, gender and age using a race classifier, a gender classifier andan age classifier successively to obtain a race property, a genderproperty and an age property of the face.

According to another embodiment, the textural-feature-extraction module303 includes a BIM-feature-extraction unit configured to extractbiologically inspired model (BIM) features within the first effectivearea image of the face. For example, the BIM-feature-extraction unit isfurther configured to: a) establish 64 sets of gabor fibers andfiltering the first effective area image of the face in 16 scales and 4orientations using the 64 sets of gabor filters to obtain a gabor image;b) divide the gabor image into 8 parts, wherein a part includes twoscales and four orientations; c) in an orientation of a part, select oneset of m×n masks and dividing the gabor image to obtain two sets ofserial gabor features; and d) compare gabor features corresponding tothe two scales of the part and taking a higher value of correspondingfeature dimensions as a final feature output; adjust sizes of the masks;and repeat the operations c)-d) for k times to obtain k×4×8 sets of BIMfeatures of the first effective area image of the face. For example,after obtaining k×4×8 sets of BIM features of the face within theeffective area image, the BIM-feature-extraction unit is furtherconfigured to perform principal component analysis (PCA) dimensionalityreduction on the k×4×8 sets of BIM features to obtain the first textualfeatures.

FIG. 6 is a simplified diagram showing the system 500 for facialproperty identification according to another embodiment of the presentinvention. This diagram is merely an example, which should not undulylimit the scope of the claims. One of ordinary skill in the art wouldrecognize many variations, alternatives, and modifications. The system500 further includes a property-classifier-training module 305configured to establish a race classifier, a gender classifiers and anage classifier successively.

According to one embodiment, the effective-area-image-acquisition module302 includes: a positioning unit 302 a configured to detect the face inthe image sample and determine eye positions on the face; and aneffective-area-image-cutting unit 302 b configured to calibrate anoriginal image of the face based on the eye positions on the face andobtain the first effective area image of the face within a predeterminedarea centering on the eyes of the face. According to another embodiment,the facial-property-identification module 304 includes: arace-property-identification unit 304 a configured to classify the firsttextural features of the first effective area image by race with a raceclassifier to obtain a race property of the face, agender-property-identification unit 304 b configured to select a genderclassifier corresponding to the race property of the face and classifythe first textual features of the first effective area image by genderwith the gender classifier to obtain a gender property of the face, andan age-property-identification unit 304 c configured to select an ageclassifier corresponding to the race property and the gender property ofthe face and classify the first textural features of the first effectivearea image by age with the age classifier to obtain an age property ofthe face.

In some embodiments, the age-property-identification unit 304 c isfurther configured to: select a first-level age classifier correspondingto the race property and the gender property of the face; input thefirst textural features of the first effective area image into thefirst-level age classifier to obtain first-level age groupscorresponding to the first effective area image and first weights of thefirst-level age groups; select a second-level age classifier accordingto a particular first-level age group with a highest first weight amongthe first-level age groups; input the first textural features of thefirst effective area image into the second-level age classifier toobtain second-level age groups corresponding to the first effective areaimage and second weights of the second-level age groups; select athird-level age classifier according to a particular second-level agegroup with a highest second weight among the second-level age groups;input the first textural features of the first effective area image intothe third-level age classifier to obtain a third-level age correspondingto the first effective area image; and obtain an age property of theface according to the third-level age, the first weights of thefirst-level age groups and the second weights of the second-level agegroups.

FIG. 7 is a simplified diagram showing the property-classifier-trainingmodule 305 as part of the system 500 according to one embodiment of thepresent invention. This diagram is merely an example, which should notunduly limit the scope of the claims. One of ordinary skill in the artwould recognize many variations, alternatives, and modifications.

According to one embodiment, the property-classifier-training module 305includes: a training-image-sample-collection-acquisition unit 305 aconfigured to acquire a training image sample collection; aneffective-area-image-acquisition unit 305 b configured to acquire asecond effective area image in an image sample in the training imagesample collection; a textural-feature-extraction unit 305 c configuredto extract second textural features of the second effective area image;and a property-classifier-training unit 305 d configured to establishthe race classifier, the gender classifier and the age classifiersuccessively based on at least information associated with the secondtextural features of the second effective area images in the trainingimage sample collection.

According to another embodiment, the property-classifier-training unit305 d comprises: a race-classifier-training subunit 305 dl configured todivide the training sample collection into white training samples, blacktraining samples and yellow training samples corresponding to a whiterace, a black race and a yellow race respectively; and train thirdtextural features of the white training samples, the black trainingsamples and the yellow training samples separately to obtain a ternaryclassifier associated with the white race, the black race and the yellowrace. According to yet another embodiment, theproperty-classifier-training unit 305 d includes agender-classifier-training subunit 305 d 2 configured to divide thewhite training samples, the black training samples and the yellowtraining samples by gender to obtain gender samples of each race andtrain fourth textural features of the gender samples of each raceseparately to obtain a gender binary classifier associated with eachrace, wherein the binary classifier corresponds to male and female.

In one embodiment, the property-classifier-training unit 305 d includesan age-classifier-training subunit 305 d 3 configured to: divide thegender samples of each race by age groups to obtain infant trainingsamples, child training samples, youth training samples and seniortraining samples; train the infant training samples, the child trainingsamples, the youth training samples and the senior training samples toestablish a first-level age classifier; train fifth textural features ofsecond-level training samples associated with an age interval of fiveyears related to the infant training samples, the child trainingsamples, the youth training samples and the senior training samples toestablish a second-level age classifier; and train a linearly-fitthird-level age classifier for the infant training samples, the childtraining samples, the youth training samples and the senior trainingsamples based on at least information associated with an age interval offive years.

According to one embodiment, a method is provided for facial propertyidentification. For example, an image sample is acquired; a firsteffective area image of a face is acquired in the image sample; firsttextural features of the first effective area image are extracted; andthe first textural features of the first effective area image areclassified by race, gender and age using a race classifier, a genderclassifier and an age classifier successively to obtain a race property,a gender property and an age property of the face. For example, themethod is implemented according to at least FIG. 1, and/or FIG. 2.

According to another embodiment, a system for facial propertyidentification includes: a test-sample-acquisition module, aneffective-area-image-acquisition module, a textural-feature-extractionmodule, and a facial-property-identification module. Thetest-sample-acquisition module is configured to acquiring an imagesample. The effective-area-image-acquisition module is configured toacquire a first effective area image of a face in the image sample. Thetextural-feature-extraction module is configured to extract firsttextural features of the first effective area image. Thefacial-property-identification module is configured to classify thefirst textural features of the first effective area image by race,gender and age using a race classifier, a gender classifier and an ageclassifier successively to obtain a race property, a gender property andan age property of the face. For example, the system is implementedaccording to at least FIG. 5, FIG. 6, and/or FIG. 7.

According to yet another embodiment, a non-transitory computer readablestorage medium includes programming instructions for facial propertyidentification. The programming instructions are configured to cause oneor more data processors to execute operations. For example, an imagesample is acquired; a first effective area image of a face is acquiredin the image sample; first textural features of the first effective areaimage are extracted; and the first textural features of the firsteffective area image are classified by race, gender and age using a raceclassifier, a gender classifier and an age classifier successively toobtain a race property, a gender property and an age property of theface. For example, the storage medium is implemented according to atleast FIG. 1, and/or FIG. 2.

The above only describes several scenarios presented by this invention,and the description is relatively specific and detailed, yet it cannottherefore be understood as limiting the scope of this invention'spatent. It should be noted that ordinary technicians in the field mayalso, without deviating from the invention's conceptual premises, make anumber of variations and modifications, which are all within the scopeof this invention. As a result, in terms of protection, the patentclaims shall prevail.

For example, some or all components of various embodiments of thepresent invention each are, individually and/or in combination with atleast another component, implemented using one or more softwarecomponents, one or more hardware components, and/or one or morecombinations of software and hardware components. In another example,some or all components of various embodiments of the present inventioneach are, individually and/or in combination with at least anothercomponent, implemented in one or more circuits, such as one or moreanalog circuits and/or one or more digital circuits. In yet anotherexample, various embodiments and/or examples of the present invention iscombined.

Additionally, the methods and systems described herein is implemented onmany different types of processing devices by program code comprisingprogram instructions that are executable by the device processingsubsystem. The software program instructions includes source code,object code, machine code, or any other stored data that is operable tocause a processing system to perform the methods and operationsdescribed herein. Other implementations may also be used, however, suchas firmware or even appropriately designed hardware configured to carryout the methods and systems described herein.

The systems' and methods' data (e.g., associations, mappings, datainput, data output, intermediate data results, final data results, etc.)is stored and implemented in one or more different types ofcomputer-implemented data stores, such as different types of storagedevices and programming constructs (e.g., RAM, ROM, Flash memory, flatfiles, databases, programming data structures, programming variables,IF-THEN (or similar type) statement constructs, etc.). It is noted thatdata structures describe formats for use in organizing and storing datain databases, programs, memory, or other computer-readable media for useby a computer program.

The systems and methods is provided on many different types ofcomputer-readable media including computer storage mechanisms (e.g.,CD-ROM, diskette, RAM, flash memory, computer's hard drive, etc.) thatcontain instructions (e.g., software) for use in execution by aprocessor to perform the methods' operations and implement the systemsdescribed herein.

The computer components, software modules, functions, data stores anddata structures described herein is connected directly or indirectly toeach other in order to allow the flow of data needed for theiroperations. It is also noted that a module or processor includes but isnot limited to a unit of code that performs a software operation, and isimplemented for example as a subroutine unit of code, or as a softwarefunction unit of code, or as an object (as in an object-orientedparadigm), or as an applet, or in a computer script language, or asanother type of computer code. The software components and, orfunctionality is located on a single computer or distributed acrossmultiple computers depending upon the situation at hand.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specifics, these should not beconstrued as limitations on the scope or of what is claimed, but ratheras descriptions of features specific to particular embodiments. Certainfeatures that are described in this specification in the context orseparate embodiments can also be implemented in combination in a singleembodiment. Conversely, various features that are described in thecontext of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features is described above as acting in certain combinationsand even initially claimed as such, one or more features from a claimedcombination can in some cases be excised from the combination, and theclaimed combination is directed to a subcombination or variation of asubcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingis advantageous. Moreover, the separation of various system componentsin the embodiments described above should not be understood as requiringsuch separation in all embodiments, and it should be understood that thedescribed program components and systems can generally be integratedtogether in a single software product or packaged into multiple softwareproducts.

Although specific embodiments of the present invention have beendescribed, it will be understood by those of skill in the art that thereare other embodiments that are equivalent to the described embodiments.Accordingly, it is to be understood that the invention is not to belimited by the specific illustrated embodiments, but only by the scopeof the appended claims.

1. A method for facial property identification, the method comprising:acquiring an image sample; acquiring a first effective area image of aface in the image sample; extracting first textural features of thefirst effective area image; and classifying the first textural featuresof the first effective area image by race, gender and age using a raceclassifier, a gender classifier and an age classifier successively toobtain a race property, a gender property and an age property of theface.
 2. The method of claim 1, further comprising: establishing a raceclassifier, a gender classifiers and an age classifier successively. 3.The method of claim 2, wherein the establishing a race classifier, agender classifiers and an age classifier successively comprises:acquiring a training image sample collection; acquiring a secondeffective area image in an image sample in the training image samplecollection; extracting second textural features of the second effectivearea image; and establishing the race classifier, the gender classifierand the age classifier successively based on at least informationassociated with the second textural features of the second effectivearea images in the training image sample collection.
 4. The method ofclaim 3, wherein the establishing the race classifier based on at leastinformation associated with the second textual features in the trainingimage sample collection comprises: dividing the training samplecollection into white training samples, black training samples andyellow training samples corresponding to a white race, a black race anda yellow race respectively; and training third textural features of thewhite training samples, the black training samples and the yellowtraining samples separately to obtain a ternary classifier associatedwith the white race, the black race and the yellow race.
 5. The methodof claim 4, wherein the establishing the gender classifier based on atleast information associated with the second textural features in thetraining image sample collection comprises: dividing the white trainingsamples, the black training samples and the yellow training samples bygender to obtain gender samples of each race; and training fourthtextural features of the gender samples of each race separately toobtain a gender binary classifier associated with each race, wherein thebinary classifier corresponds to male and female.
 6. (canceled)
 7. Themethod of claim 1, wherein the acquiring a first effective area image ofa face in the image sample includes: detecting the face in the imagesample; determining eye positions on the face; calibrating an originalimage of the face based on the eye positions on the face; and obtainingthe first effective area image of the face within a predetermined areacentering on the eyes of the face.
 8. The method of claim 1, wherein theextracting first textural features of the first effective area imageincludes: extracting biologically inspired model (BIM) features withinthe first effective area image of the face.
 9. The method of claim 8,wherein the extracting BIM features within the first effective areaimage of the face comprises: establishing step, establishing 64 sets ofgabor filters and filtering the first effective area image of the facein 16 scales and 4 orientations using the 64 sets of gabor filters toobtain a gabor image; dividing step, dividing the gabor image into 8parts, wherein a part includes two scales and four orientations;selecting step, in an orientation of a part, selecting one set of m×nmasks and dividing the gabor image to obtain two sets of serial gaborfeatures; and comparison step, comparing gabor features corresponding tothe two scales of the part and taking a higher value of correspondingfeature dimensions as a final feature output; adjusting step, adjustingsizes of the masks; and repeating the operations the selecting step tothe comparison step for k times to obtain k×4×8 sets of BIM features ofthe first effective area image of the face.
 10. The method of claim 9,further comprising: performing principal component analysis (PCA)dimensionality reduction on the k×4×8 sets of BIM features to obtain thefirst textual features.
 11. The method of claim 1, wherein theclassifying the first textural features of the first effective areaimage by race, gender and age using a race classifier, a genderclassifier and an age classifier successively to obtain a race property,a gender property and an age property of the face comprises: classifyingthe first textural features of the first effective area image by racewith a race classifier to obtain a race property of the face; selectinga gender classifier corresponding to the race property of the face;classifying the first textual features of the first effective area imageby gender with the gender classifier to obtain a gender property of theface; selecting an age classifier corresponding to the race property andthe gender property of the face; and classifying the first texturalfeatures of the first effective area image by age with the ageclassifier to obtain an age property of the face.
 12. (canceled)
 13. Asystem for facial property identification, comprising: atest-sample-acquisition module configured to acquiring an image sample;an effective-area-image-acquisition module configured to acquire a firsteffective area image of a face in the image sample; atextural-feature-extraction module configured to extract first texturalfeatures of the first effective area image; and afacial-property-identification module configured to classify the firsttextural features of the first effective area image by race, gender andage using a race classifier, a gender classifier and an age classifiersuccessively to obtain a race property, a gender property and an ageproperty of the face.
 14. The system of claim 13, further comprising: aproperty-classifier-training module configured to establish a raceclassifier, a gender classifiers and an age classifier successively. 15.The system of claim 14, wherein the property-classifier-training moduleincludes: a training-image-sample-collection-acquisition unit configuredto acquire a training image sample collection; aneffective-area-image-acquisition unit configured to acquire a secondeffective area image in an image sample in the training image samplecollection; a textural-feature-extraction unit configured to extractsecond textural features of the second effective area image; and aproperty-classifier-training unit configured to establish the raceclassifier, the gender classifier and the age classifier successivelybased on at least information associated with the second texturalfeatures of the second effective area images in the training imagesample collection.
 16. The system of claim 15, wherein theproperty-classifier-training unit comprises: a race-classifier-trainingsubunit configured to divide the training sample collection into whitetraining samples, black training samples and yellow training samplescorresponding to a white race, a black race and a yellow racerespectively; and train third textural features of the white trainingsamples, the black training samples and the yellow training samplesseparately to obtain a ternary classifier associated with the whiterace, the black race and the yellow race.
 17. The system of claim 16,wherein the property-classifier-training unit comprises: agender-classifier-training subunit configured to divide the whitetraining samples, the black training samples and the yellow trainingsamples by gender to obtain gender samples of each race and train fourthtextural features of the gender samples of each race separately toobtain a gender binary classifier associated with each race, wherein thebinary classifier corresponds to male and female.
 18. (canceled)
 19. Thesystem of claim 13, wherein the effective-area-image-acquisition modulecomprises: a positioning unit configured to detect the face in the imagesample and determine eye positions on the face; and aneffective-area-image-cutting unit configured to calibrate an originalimage of the face based on the eye positions on the face and obtain thefirst effective area image of the face within a predetermined areacentering on the eyes of the face.
 20. The system of claim 13, whereinthe textural-feature-extraction module comprises: aBIM-feature-extraction unit configured to extract biologically inspiredmodel (BIM) features within the first effective area image of the face.21. The system of claim 20, wherein the BIM-feature-extraction unit isfurther configured to: a) establish 64 sets of gabor filters andfiltering the first effective area image of the face in 16 scales and 4orientations using the 64 sets of gabor filters to obtain a gabor image;b) divide the gabor image into 8 parts, wherein a part includes twoscales and four orientations; c) in an orientation of a part, select oneset of m×n masks and dividing the gabor image to obtain two sets ofserial gabor features; and d) compare gabor features corresponding tothe two scales of the part and taking a higher value of correspondingfeature dimensions as a final feature output; adjust sizes of the masks;and repeat the operations c)-d) for k times to obtain k×4×8 sets of BIMfeatures of the first effective area image of the face.
 22. The systemof claim 21, wherein the BIM-feature-extraction unit is furtherconfigured to perform principal component analysis (PCA) dimensionalityreduction on the k×4×8 sets of BIM features to obtain the first textualfeatures.
 23. The system of claim 13, wherein thefacial-property-identification module comprises: arace-property-identification unit configured to classify the firsttextural features of the first effective area image by race with a raceclassifier to obtain a race property of the face; agender-property-identification unit configured to select a genderclassifier corresponding to the race property of the face and classifythe first textual features of the first effective area image by genderwith the gender classifier to obtain a gender property of the face; andan age-property-identification unit configured to select an ageclassifier corresponding to the race property and the gender property ofthe face and classify the first textural features of the first effectivearea image by age with the age classifier to obtain an age property ofthe face.
 24. (canceled)
 25. The system of claim 13, further comprising:one or more data processors; and a computer-readable storage medium;wherein one or more of the test-sample-acquisition module, theeffective-area-image-acquisition module, the textural-feature-extractionmodule, and the facial-property-identification module are stored in thestorage medium and configured to be executed by the one or more dataprocessors.
 26. A non-transitory computer readable storage mediumcomprising programming instructions for facial property identification,the programming instructions configured to cause one or more dataprocessors to execute operations comprising: acquiring an image sample;acquiring a first effective area image of a face in the image sample;extracting first textural features of the first effective area image;and classifying the first textural features of the first effective areaimage by race, gender and age using a race classifier, a genderclassifier and an age classifier successively to obtain a race property,a gender property and an age property of the face.